ArticlemSystems2021
Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation.
Article in mSystems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
- Gut microbiome composition and functional potential associate with incident type 2 diabetes in 4,685 adults from a Swedish prospective cohort.Cell reports. Medicine · 2026Article
- A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument.Gut microbes · 2025Article
- [18F]flutemetamol uptake in the colon of a memory clinic population and its association with brain amyloidosis and the gut microbiota profile: an exploratory study.European journal of nuclear medicine and molecular imaging · 2025Article
- Artificial Intelligence in Personalized Medicine for Diabetes Mellitus: A Narrative Review.Cureus · 2025Review
- Dietary Nutrition, Gut Microbiota, and Health Status Across Geographically Diverse Populations in Mongolia: A Cross-Sectional Study.Food science & nutrition · 2025Article
- Approaches to predict future type 2 diabetes mellitus and chronic kidney disease: A scoping review.PloS one · 2025Article
- A scoping review of artificial intelligence-based methods for diabetes risk prediction.NPJ digital medicine · 2023Article
- The number of metabolic syndrome risk factors predicts alterations in gut microbiota in Chinese children from the Huantai study.BMC pediatrics · 2023Article
- The gut microbiome: a core regulator of metabolism.The Journal of endocrinology · 2023Review
- Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis.Frontiers in microbiology · 2023Article
- Article
- Dietary ε-Polylysine Affects on Gut Microbiota and Plasma Metabolites Profiling in Mice.Frontiers in nutrition · 2022Article
- Gut Microbiota and Targeted Biomarkers Analysis in Patients With Cognitive Impairment.Frontiers in neurology · 2022Article
- Wholegrain fermentation affects gut microbiota composition, phenolic acid metabolism and pancreatic beta cell function in a rodent model of type 2 diabetes.Frontiers in microbiology · 2022Article
- Evaluating the clinical relevance of the enterotypes in the Estonian microbiome cohort.Frontiers in genetics · 2022Article
- Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
Abstract
The incidence of type 2 diabetes (T2D) has been increasing globally, and a growing body of evidence links type 2 diabetes with altered microbiota composition. Type 2 diabetes is preceded by a long prediabetic state characterized by changes in various metabolic parameters. We tested whether the gut microbiome could have predictive potential for T2D development during the healthy and prediabetic disease stages. We used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome in Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5 year) and longer (4 year) period. Our results show that the inclusion of the gut microbiome improves prediction accuracy for modeling T2D-associated parameters such as glycosylated hemoglobin and insulin measures. We identified novel microbial biomarkers and described their effects on the predictions using interpretable machine learning techniques, which revealed complex linear and nonlinear associations. Additionally, the modeling strategy carried out allowed us to compare the stability of model performance and biomarker selection, also revealing differences in short-term and long-term predictions. The identified microbiome biomarkers provide a predictive measure for various metabolic traits related to T2D, thus providing an additional parameter for personal risk assessment. Our work also highlights the need for robust modeling strategies and the value of interpretable machine learning.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.